Catalog query framework on distributed key value store

ABSTRACT

Techniques for executing show commands are described herein. A plurality of navigation steps is utilized, each navigation step corresponding to a different layer in a database structure and each navigation step including an operator to fetch items from a metadata database up to respective bounded limits. Dependency information is also fetched for objects of the specified object type in the show command. After a set of objects from the last layer are processed, memory for the navigation steps is flushed and the next set of objects are processed.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Continuation of U.S. Pat. Application Serial No.17/514,227, filed Oct. 29, 2021, the contents of which are incorporatedherein by reference in their entirety.

TECHNICAL FIELD

The present disclosure generally relates databases and more particularlyto efficiently processing show commands related to databases.

BACKGROUND

Show commands can be used to display metadata information about databaseobjects. However, most database systems do not provide structuredframeworks for executing show commands. As such, processing showcommands for large amounts of data can be slow due to inefficient use ofmemory. This may lead to deteriorating performance and system crashes.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 illustrates an example computing environment in which a clouddatabase system, according to some example embodiments.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, according to some example embodiments.

FIG. 3 is a block diagram illustrating components of an executionplatform, according to some example embodiments.

FIG. 4 illustrates a high-level block diagram of a framework to executeshow commands, according to some example embodiments.

FIG. 5 illustrates a flow diagram for a method for processing anavigation phase in execution of a show command, according to someexample embodiments.

FIG. 6 illustrates a flow diagram for a method for fetching dependencyinformation, according to some example embodiments.

FIG. 7 illustrates an example of an output table in response to a showcommand, according to some example embodiments.

FIG. 8 illustrates a high-level block diagram of a framework to executeshow commands with metric tracking, according to some exampleembodiments.

FIG. 9 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Described herein are embodiments of frameworks to efficiently processshow commands. The frameworks can include a navigation phase and adependency fetching phase. The navigation phase can be configured in astepwise fashion where each step corresponds to a different layer in thedatabase structure. Each step may have a bounded memory limit, such thatthe navigation does not overwhelm memory use. The framework allowsdevelopers and users to configure dependency fetching elements for theinformation to be included in the output of different show commands. Aresult dependency manager can set up transactions with a metadata storeto efficiently process dependency fetching. Moreover, different metricscan be tracked during execution of show commands to identify possibleissues and bottleneck areas.

FIG. 1 illustrates an example shared data processing platform 100. Toavoid obscuring the inventive subject matter with unnecessary detail,various functional components that are not germane to conveying anunderstanding of the inventive subject matter have been omitted from thefigures. However, a skilled artisan will readily recognize that variousadditional functional components may be included as part of the shareddata processing platform 100 to facilitate additional functionality thatis not specifically described herein.

As shown, the shared data processing platform 100 comprises thenetwork-based database system 102, a cloud computing storage platform104 (e.g., a storage platform, an AWS® service, Microsoft Azure®, orGoogle Cloud Services®), and a remote computing device 106. Thenetwork-based database system 102 is a cloud database system used forstoring and accessing data (e.g., internally storing data, accessingexternal remotely located data) in an integrated manner, and reportingand analysis of the integrated data from the one or more disparatesources (e.g., the cloud computing storage platform 104). The cloudcomputing storage platform 104 comprises a plurality of computingmachines and provides on-demand computer system resources such as datastorage and computing power to the network-based database system 102.While in the embodiment illustrated in FIG. 1 , a data warehouse isdepicted, other embodiments may include other types of databases orother data processing systems.

The remote computing device 106 (e.g., a user device such as a laptopcomputer) comprises one or more computing machines (e.g., a user devicesuch as a laptop computer) that execute a remote software component 108(e.g., browser accessed cloud service) to provide additionalfunctionality to users of the network-based database system 102. Theremote software component 108 comprises a set of machine-readableinstructions (e.g., code) that, when executed by the remote computingdevice 106, cause the remote computing device 106 to provide certainfunctionality. The remote software component 108 may operate on inputdata and generates result data based on processing, analyzing, orotherwise transforming the input data. As an example, the remotesoftware component 108 can be a data provider or data consumer thatenables database tracking procedures, such as streams on shared tablesand views.

The network-based database system 102 comprises an access managementsystem 110, a compute service manager 112, an execution platform 114,and a database 116. The access management system 110 enablesadministrative users to manage access to resources and services providedby the network-based database system 102. Administrative users cancreate and manage users, roles, and groups, and use permissions to allowor deny access to resources and services. The access management system110 can store shared data that securely manages shared access to thestorage resources of the cloud computing storage platform 104 amongstdifferent users of the network-based database system 102, as discussedin further detail below.

The compute service manager 112 coordinates and manages operations ofthe network-based database system 102. The compute service manager 112also performs query optimization and compilation as well as managingclusters of computing services that provide compute resources (e.g.,virtual warehouses, virtual machines, EC2 clusters). The compute servicemanager 112 can support any number of client accounts such as end usersproviding data storage and retrieval requests, system administratorsmanaging the systems and methods described herein, and othercomponents/devices that interact with compute service manager 112.

The compute service manager 112 is also coupled to database 116, whichis associated with the entirety of data stored on the shared dataprocessing platform 100. The database 116 stores data pertaining tovarious functions and aspects associated with the network-based databasesystem 102 and its users.

In some embodiments, database 116 includes a summary of data stored inremote data storage systems as well as data available from one or morelocal caches. Additionally, database 116 may include informationregarding how data is organized in the remote data storage systems andthe local caches. Database 116 allows systems and services to determinewhether a piece of data needs to be accessed without loading oraccessing the actual data from a storage device. The compute servicemanager 112 is further coupled to an execution platform 114, whichprovides multiple computing resources (e.g., virtual warehouses) thatexecute various data storage and data retrieval tasks, as discussed ingreater detail below.

Execution platform 114 is coupled to multiple data storage devices 124-1to 124-N that are part of a cloud computing storage platform 104. Insome embodiments, data storage devices 124-1 to 124-N are cloud-basedstorage devices located in one or more geographic locations. Forexample, data storage devices 124-1 to 124-N may be part of a publiccloud infrastructure or a private cloud infrastructure. Data storagedevices 124-1 to 124-N may be hard disk drives (HDDs), solid statedrives (SSDs), storage clusters, Amazon S3 storage systems or any otherdata storage technology. Additionally, cloud computing storage platform104 may include distributed file systems (such as Hadoop DistributedFile Systems (HDFS)), object storage systems, and the like.

The execution platform 114 comprises a plurality of compute nodes (e.g.,virtual warehouses). A set of processes on a compute node executes aquery plan compiled by the compute service manager 112. The set ofprocesses can include: a first process to execute the query plan; asecond process to monitor and delete micro-partition files using a leastrecently used (LRU) policy, and implement an out of memory (OOM) errormitigation process; a third process that extracts health informationfrom process logs and status information to send back to the computeservice manager 112; a fourth process to establish communication withthe compute service manager 112 after a system boot; and a fifth processto handle all communication with a compute cluster for a given jobprovided by the compute service manager 112 and to communicateinformation back to the compute service manager 112 and other computenodes of the execution platform 114.

The cloud computing storage platform 104 also comprises an accessmanagement system 118 and a web proxy 120. As with the access managementsystem 110, the access management system 118 allows users to create andmanage users, roles, and groups, and use permissions to allow or denyaccess to cloud services and resources. The access management system 110of the network-based database system 102 and the access managementsystem 118 of the cloud computing storage platform 104 can communicateand share information so as to enable access and management of resourcesand services shared by users of both the network-based database system102 and the cloud computing storage platform 104. The web proxy 120handles tasks involved in accepting and processing concurrent API calls,including traffic management, authorization and access control,monitoring, and API version management. The web proxy 120 provides HTTPproxy service for creating, publishing, maintaining, securing, andmonitoring APIs (e.g., REST APIs).

In some embodiments, communication links between elements of the shareddata processing platform 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-Networks) coupled to oneanother. In alternative embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

As shown in FIG. 1 , data storage devices 124-1 to 124-N are decoupledfrom the computing resources associated with the execution platform 114.That is, new virtual warehouses can be created and terminated in theexecution platform 114 and additional data storage devices can becreated and terminated on the cloud computing storage platform 104 in anindependent manner. This architecture supports dynamic changes to thenetwork-based database system 102 based on the changing datastorage/retrieval needs as well as the changing needs of the users andsystems accessing the shared data processing platform 100. The supportof dynamic changes allows network-based database system 102 to scalequickly in response to changing demands on the systems and componentswithin network-based database system 102. The decoupling of thecomputing resources from the data storage devices 124-1 to 124-Nsupports the storage of large amounts of data without requiring acorresponding large amount of computing resources. Similarly, thisdecoupling of resources supports a significant increase in the computingresources utilized at a particular time without requiring acorresponding increase in the available data storage resources.Additionally, the decoupling of resources enables different accounts tohandle creating additional compute resources to process data shared byother users without affecting the other users’ systems. For instance, adata provider may have three compute resources and share data with adata consumer, and the data consumer may generate new compute resourcesto execute queries against the shared data, where the new computeresources are managed by the data consumer and do not affect or interactwith the compute resources of the data provider.

Compute service manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing device 106 areshown in FIG. 1 as individual components. However, each of computeservice manager 112, database 116, execution platform 114, cloudcomputing storage platform 104, and remote computing environment may beimplemented as a distributed system (e.g., distributed across multiplesystems/platforms at multiple geographic locations) connected by APIsand access information (e.g., tokens, login data). Additionally, each ofcompute service manager 112, database 116, execution platform 114, andcloud computing storage platform 104 can be scaled up or down(independently of one another) depending on changes to the requestsreceived and the changing needs of shared data processing platform 100.Thus, in the described embodiments, the network-based database system102 is dynamic and supports regular changes to meet the current dataprocessing needs.

During typical operation, the network-based database system 102processes multiple jobs (e.g., queries) determined by the computeservice manager 112. These jobs are scheduled and managed by the computeservice manager 112 to determine when and how to execute the job. Forexample, the compute service manager 112 may divide the job intomultiple discrete tasks and may determine what data is needed to executeeach of the multiple discrete tasks. The compute service manager 112 mayassign each of the multiple discrete tasks to one or more nodes of theexecution platform 114 to process the task. The compute service manager112 may determine what data is needed to process a task and furtherdetermine which nodes within the execution platform 114 are best suitedto process the task. Some nodes may have already cached the data neededto process the task (due to the nodes having recently downloaded thedata from the cloud computing storage platform 104 for a previous job)and, therefore, be a good candidate for processing the task. Metadatastored in the database 116 assists the compute service manager 112 indetermining which nodes in the execution platform 114 have alreadycached at least a portion of the data needed to process the task. One ormore nodes in the execution platform 114 process the task using datacached by the nodes and, if necessary, data retrieved from the cloudcomputing storage platform 104. It is desirable to retrieve as much dataas possible from caches within the execution platform 114 because theretrieval speed is typically much faster than retrieving data from thecloud computing storage platform 104.

As shown in FIG. 1 , the shared data processing platform 100 separatesthe execution platform 114 from the cloud computing storage platform104. In this arrangement, the processing resources and cache resourcesin the execution platform 114 operate independently of the data storagedevices 124-1 to 124-N in the cloud computing storage platform 104.Thus, the computing resources and cache resources are not restricted tospecific data storage devices 124-1 to 124-N. Instead, all computingresources and all cache resources may retrieve data from, and store datato, any of the data storage resources in the cloud computing storageplatform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 112, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2 , a request processing service 202manages received data storage requests and data retrieval requests(e.g., jobs to be performed on database data). For example, the requestprocessing service 202 may determine the data necessary to process areceived query (e.g., a data storage request or data retrieval request).The data may be stored in a cache within the execution platform 114 orin a data storage device in cloud computing storage platform 104. Amanagement console service 204 supports access to various systems andprocesses by administrators and other system managers. Additionally, themanagement console service 204 may receive a request to execute a joband monitor the workload on the system. The stream share engine 225manages change tracking on database objects, such as a data share (e.g.,shared table) or shared view, according to some example embodiments.

The compute service manager 112 also includes a job compiler 206, a joboptimizer 208, and a job executor 210. The job compiler 206 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 208 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 208 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor210 executes the execution code for jobs received from a queue ordetermined by the compute service manager 112.

A job scheduler and coordinator 212 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 114. For example, jobs may beprioritized and processed in that prioritized order. In an embodiment,the job scheduler and coordinator 212 determines a priority for internaljobs that are scheduled by the compute service manager 112 with other“outside” jobs such as user queries that may be scheduled by othersystems in the database but may utilize the same processing resources inthe execution platform 114. In some embodiments, the job scheduler andcoordinator 212 identifies or assigns particular nodes in the executionplatform 114 to process particular tasks. A virtual warehouse manager214 manages the operation of multiple virtual warehouses implemented inthe execution platform 114. As discussed below, each virtual warehouseincludes multiple execution nodes that each include a cache and aprocessor (e.g., a virtual machine, an operating system level containerexecution environment).

Additionally, the compute service manager 112 includes a configurationand metadata manager 216, which manages the information related to thedata stored in the remote data storage devices and in the local caches(i.e., the caches in execution platform 114). The configuration andmetadata manager 216 uses the metadata to determine which datamicro-partitions need to be accessed to retrieve data for processing aparticular task or job. A monitor and workload analyzer 218 overseesprocesses performed by the compute service manager 112 and manages thedistribution of tasks (e.g., workload) across the virtual warehouses andexecution nodes in the execution platform 114. The monitor and workloadanalyzer 218 also redistributes tasks, as needed, based on changingworkloads throughout the network-based database system 102 and mayfurther redistribute tasks based on a user (e.g., “external”) queryworkload that may also be processed by the execution platform 114. Theconfiguration and metadata manager 216 and the monitor and workloadanalyzer 218 are coupled to a data storage device 220. Data storagedevice 220 in FIG. 2 represent any data storage device within thenetwork-based database system 102. For example, data storage device 220may represent caches in execution platform 114, storage devices in cloudcomputing storage platform 104, or any other storage device.

FIG. 3 is a block diagram illustrating components of the executionplatform 114, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3 , execution platform 114 includesmultiple virtual warehouses, which are elastic clusters of computeinstances, such as virtual machines. In the example illustrated, thevirtual warehouses include virtual warehouse 1, virtual warehouse 2, andvirtual warehouse n. Each virtual warehouse (e.g., EC2 cluster) includesmultiple execution nodes (e.g., virtual machines) that each include adata cache and a processor. The virtual warehouses can execute multipletasks in parallel by using the multiple execution nodes. As discussedherein, execution platform 114 can add new virtual warehouses and dropexisting virtual warehouses in real time based on the current processingneeds of the systems and users. This flexibility allows the executionplatform 114 to quickly deploy large amounts of computing resources whenneeded without being forced to continue paying for those computingresources when they are no longer needed. All virtual warehouses canaccess data from any data storage device (e.g., any storage device incloud computing storage platform 104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary (e.g., upon a query or jobcompletion).

Each virtual warehouse is capable of accessing any of the data storagedevices 124-1 to 124-N shown in FIG. 1 . Thus, the virtual warehousesare not necessarily assigned to a specific data storage device 124-1 to124-N and, instead, can access data from any of the data storage devices124-1 to 124-N within the cloud computing storage platform 104.Similarly, each of the execution nodes shown in FIG. 3 can access datafrom any of the data storage devices 124-1 to 124-N. For instance, thestorage device 124-1 of a first user (e.g., provider account user) maybe shared with a worker node in a virtual warehouse of another user(e.g., consumer account user), such that the other user can create adatabase (e.g., read-only database) and use the data in storage device124-1 directly without needing to copy the data (e.g., copy it to a newdisk managed by the consumer account user). In some embodiments, aparticular virtual warehouse or a particular execution node may betemporarily assigned to a specific data storage device, but the virtualwarehouse or execution node may later access data from any other datastorage device.

In the example of FIG. 3 , virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-N. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-N includes a cache 304-N and aprocessor 306-N. Each execution node 302-1, 302-2, and 302-N isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-N. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-Nincludes a cache 314-N and a processor 316-N. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-N.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-N includes a cache 324-N and a processor 326-N.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data the execution nodes are caching. For example,these execution nodes do not store or otherwise maintain stateinformation about the execution node, or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each include one data cacheand one processor, alternative embodiments may include execution nodescontaining any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node(e.g., local disk), data that was retrieved from one or more datastorage devices in cloud computing storage platform 104 (e.g., S3objects recently accessed by the given node). In some exampleembodiments, the cache stores file headers and individual columns offiles as a query downloads only columns necessary for that query.

To improve cache hits and avoid overlapping redundant data stored in thenode caches, the job optimizer 208 assigns input file sets to the nodesusing a consistent hashing scheme to hash over table file names of thedata accessed (e.g., data in database 116 or database 122). Subsequentor concurrent queries accessing the same table file will therefore beperformed on the same node, according to some example embodiments.

As discussed, the nodes and virtual warehouses may change dynamically inresponse to environmental conditions (e.g., disaster scenarios),hardware/software issues (e.g., malfunctions), or administrative changes(e.g., changing from a large cluster to smaller cluster to lower costs).In some example embodiments, when the set of nodes changes, no data isreshuffled immediately. Instead, the least recently used replacementpolicy is implemented to eventually replace the lost cache contents overmultiple jobs. Thus, the caches reduce or eliminate the bottleneckproblems occurring in platforms that consistently retrieve data fromremote storage systems. Instead of repeatedly accessing data from theremote storage devices, the systems and methods described herein accessdata from the caches in the execution nodes, which is significantlyfaster and avoids the bottleneck problem discussed above. In someembodiments, the caches are implemented using high-speed memory devicesthat provide fast access to the cached data. Each cache can store datafrom any of the storage devices in the cloud computing storage platform104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the execution platform 114implements skew handling to distribute work amongst the cache resourcesand computing resources associated with a particular execution, wherethe distribution may be further based on the expected tasks to beperformed by the execution nodes. For example, an execution node may beassigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity. Further, some nodes maybe executing much slower than others due to various issues (e.g.,virtualization issues, network overhead). In some example embodiments,the imbalances are addressed at the scan level using a file stealingscheme. In particular, whenever a node process completes scanning itsset of input files, it requests additional files from other nodes. Ifthe one of the other nodes receives such a request, the node analyzesits own set (e.g., how many files are left in the input file set whenthe request is received), and then transfers ownership of one or more ofthe remaining files for the duration of the current job (e.g., query).The requesting node (e.g., the file stealing node) then receives thedata (e.g., header data) and downloads the files from the cloudcomputing storage platform 104 (e.g., from data storage device 124-1),and does not download the files from the transferring node. In this way,lagging nodes can transfer files via file stealing in a way that doesnot worsen the load on the lagging nodes.

Although virtual warehouses 1, 2, and N are associated with the sameexecution platform 114, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-N at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 114 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 114 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain cloud computing storage platform 104, but each virtual warehouse hasits own execution nodes with independent processing and cachingresources. This configuration allows requests on different virtualwarehouses to be processed independently and with no interferencebetween the requests. This independent processing, combined with theability to dynamically add and remove virtual warehouses, supports theaddition of new processing capacity for new users without impacting theperformance observed by the existing users.

As described herein, large amounts of data may be stored in the databasesystem. Thus, users may wish to obtain information regarding the data,e.g., metadata. One technique to obtain stored metadata is to use a showcommand, which is an external interface to query metadata. A showcommand is a DDL command that lists existing objects for the specifiedobject type. Examples of show command include “show tables,” “showschemas,” “show views,” etc. The output of a show command includesmetadata for the specified object type. This metadata is typicallystored in a metadata store (also referred to as metadata databaseherein).

FIG. 4 illustrates a high-level block diagram of a framework 400 toexecute show commands, according to some embodiments. The framework 400includes two phases: a navigation phase and a result-dependency fetchingphase. The navigation phase may include navigation steps 402.1-402.n,and the result-dependency fetching phase may include a result dependencymanager 404 and dependency fetcher elements 406.1-406.m.

The navigation phase corresponds to the steps taken to find entities forthe specified object type in the relevant domain given the startingsource. In framework 400, the navigation phase is provided in a stepwisefashion where each step in the navigation phase has a memory limit. Thenavigation is broken down in multiple steps so that after certainstep(s), the memory being used can be flushed and the next set of stepscan be performed with the freed-up memory.

The framework 400 may include multiple layer navigations steps402.1-402.n, where navigation step 402.1 corresponds to a firstnavigation layer and 402.n corresponds to the last navigation layer. Thenavigation steps are defined by the specified object type in the showcommand. Let’s consider the example of “show tables in account.” Here,there will be three navigation layers: database(s) to schema(s) totable(s). Hence, the first navigation step 402.1 would correspond todatabases such as db1, db2, db3, etc.; the second navigation step 402.2would correspond to schemas for each database such as schemal, schema2,schema3, etc.; and the third navigation step 402.3 would correspond totables in each schema such as table1, table2, table3, etc.

The framework 400 takes control of the navigation by providing thenavigation operators representing the navigation of a specific objecttype. Operators may be chained to become a navigation path. Duringruntime, each operator can fetch dpos (data persistent objects) to apredefined limit (e.g., memory limit) and yield fetching to otheroperators when that limit is reached. This allows dpo fetching to bescoped at each layer or level. A dpo can be a memory representation ofmetadata about an object (e.g., a database can have a dpo associatedwith it, a schema can have a dpo associated with it, a table can have adpo associated with it, etc.).

Let’s consider the example of “show tables,” again. As discussed above,there are three layers: databases to schemas to tables. Now, consider adifferent variation of “show tables” in database X, a particulardatabase. Here, there are two layers to expand under database X: schemasand tables. Two operators may be created, and each would be allocated abounded dpo count to fetch. The table operator can fetch up to a boundedlimit (e.g., 10k tables) at a time, and the schema operator can fetch upto a bounded limit (e.g., 10k schemas) at a time. In some embodiments,the bounded limit for the steps may be different.

During the show command execution, the schema operator would fetch thefirst 10k schemas, and then yield the fetching to the table operator tostart fetching under the loaded tables. When the table operator finishesloading the tables (e.g., 10k tables), it can flush the results to aresult stage (after the result dependency fetching phase as described infurther detail below) and free up the memory. After all tables under thefirst 10k schemas are scanned and flushed, the schema operator can fetchthe next 10k schemas and yield fetching to the table operator to startfetching the tables under the newly fetched schemas 10k at a time. Thisprocess may continue until all tables under all schemas of database Xare scanned and flushed, or until a total result limit has been reachedand the show command execution has been stopped.

FIG. 5 illustrates a flow diagram for a method 500 for processing anavigation phase in execution of a show command, according to someexample embodiments. In this example, three layers are described forillustration purposes only, and other numbers of layers may be involvedin execution of a show command. The number of layers depends on thespecified object type of the respective show command.

At operation 502, the system (e.g., global service (GS), compute servicemanager 112) may fetch a bounded amount of dpos for the first layer(e.g., 10k databases). At operation 504, the system may fetch a boundedamount of dpos for the second layer corresponding to the fetched resultsof the first layer (e.g., 10k schemas under the fetched 10k databases).At operation 506, the system may fetch a bounded amount of dpos for thethird or last layer corresponding to the fetched results of the secondlayer (e.g., 10k tables under the fetched 10k schemas).

At operation 508, the system may process the fetched dpos of the lastlayer (e.g., 10k tables). The processing can involve fetching resultdependencies and outputting the results to a remote result stage asdescribed in further detail below.

At operation 510, the system may check if there is any more third-layerdata under the fetched second layer (e.g., are there any more tables tobe processed under the fetched 10k schemas?). If the answer is yes,there is more data (e.g., tables) under the fetched second layer, thesystem may flush the memory and delete the previously fetched data ofthe first layer at operation 512 and then return to operation 506 andmay fetch the next set of data for the third layer.

If the answer to operation 510 is no, the system, at operation 514, maycheck if there is any more second-layer data under the fetched firstlayer (e.g., are there any more schemas to be processed under thefetched 10k databases?). If the answer is yes, there is more data (e.g.,schemas) under the fetched first layer, the system may flush the memoryand delete the previously fetched data for the second layer at operation516 and then return to operation 504 and may fetch the next set of datafor the second layer.

If the answer to operation 514 is no, the system, at operation 518, maycheck if there is any more data for the first layer (e.g., are there anymore databases?). If the answer is yes, there is more data (e.g.,databases) associated with first layer, the system may flush the memoryand delete the previously fetched data for the first layer at operation520 and then return to operation 502 and may fetch the next set of datafor the first layer.

If the answer to operation 518 is no, then all data for the show commandhas been processed and the method 500 may end (operation 522).

In this example, method 500 continued until all data for the showcommand was processed, but in some embodiments, there may be a totalresult limit set for the execution of the show command and the method500 may end when that limit is reached. In that case, bookmarks may beused to designate where the show command was stopped (e.g., the showcommand was stopped after processing db3, schema5, table10). Hence, withthe use of bookmarks, the user may execute another show command tocontinue the search where the previous search ended.

Moreover, each navigation step may employ concurrent point lookup tofurther optimize the use of resources. That is, each navigation step mayperform a range scan to determine the relationship between layers (e.g.,db to schema), and then, instead of performing individual point lookupsof the dpos, the system may perform lookups in each navigation step bysending the requests concurrently to further optimize resource usage.

Referring back to FIG. 4 , the result dependency fetching phase of theframework 400 may include the result dependency manager 404 and theplurality of dependency fetcher elements 406.1-406.m. The resultdependency manager 404 may receive the data from the last layer of thenavigation step 402.n. The result dependency manager 404 may then, inconjunction with the dependency fetcher elements 406.1-406.m, fetchextra dependency information from the metadata store and compile theresults. The results may then be output to a result stage 408 for theuser. The result stage 408 may be remote from the framework 400.

Different and diverse options may be provided for which dependencyinformation is outputted and the options may be related to the specifiedobject type in the show command. The dependency fetcher elements406.1-406.m may be configurable by developers/users. That is,developers/users may configure which dependency information is to beincluded in respective show commands.

However, the result dependency manager 404 may control the interactionof the dependency fetcher elements 406.1-406.m and the metadata store.That is, the result dependency manager 404 may control the lifecycle oftransactions with the metadata store (also referred to as DBtransactions). In some embodiments, each show command may include one DBtransaction with bulk reads so dependency information is fetchedconcurrently (not one-by-one using individual point lookups). In someembodiments, each dependency fetcher element 406.1-406.m may beassociated with a respective DB transaction and may fetch relevantdependency information using bulk reads in the respective DBtransaction. The result dependency manager 404 may receive thedependency information from the dependency fetcher elements 406.1-406.mand may populate the results with the dependency information.

FIG. 6 illustrates a flow diagram for a method 600 for fetchingdependency information, according to some example embodiments. Atoperation 602, the system may receive the data from the last layer ofthe navigation step (e.g., table data for a show table command). Atoperation 604, the system may also retrieve dependency fetchinginformation; the type of dependency information may be preconfigured bythe developer/user. For example, a developer/user may indicate that fora show tables command, the following dependency information should beobtained: search index information, last modified information, parameterinformation.

At operation 606, the system may initiate a DB transaction correspondingto the show command that is being executed. Using the DB transaction,the system may perform bulk reads to fetch the dependency informationdesignated by the developer/user.

At operation 608, the system may compile the dependency information andmay arrange the information in a table for presentation to the user.

FIG. 7 illustrates an example of an output table 700 in response to ashow command, according to some embodiments. In this example, the outputtable 700 corresponds to the output of a “show tables” command. Here,output table 700 may include columns for different properties andmetadata. The information in the columns may be classified in twogroups. A first group of columns 702 may include information retrieveddirectly from the objects that are subject of the show command. In theshow tables command, this group of columns 702 may include name,database name, schema name, etc. A second group of columns 704 mayinclude information fetched from dependency information associated withthe objects that are subject of the show command. This second group ofcolumns 704 may include information such as search optimizationinformation (e.g., search index), last modified, etc. For example, thisdependency information may be populated by the result dependency manageras described above. The number and type of columns in the output table700 may be configurable. Based on the columns set for the output table700, the result dependency fetcher elements may be configured to obtainthe relevant information.

FIG. 8 illustrates a high-level block diagram of a framework 800 toexecute show commands with metric tracking, according to someembodiments. In addition to the navigation phase (e.g., navigation steps402.1-402.n) and the result dependency fetching phase (e.g., resultdependency manager 404 and dependency fetcher elements 406.1-406.m),which are described above, framework 800 includes metrics tracking withtracker 802. Tracker 802 may be provided as a centralized metricscollector. Tracker 802 may receive performance metrics from differentcomponents in framework 800 to identify performance issues, such asbottlenecks at operator levels. For example, the navigation steps402.1-402.n may report time spent at each step and the number of dposfetched at each step to the tracker 802. Thus, bottlenecks at differentnavigation steps may be identified. The navigation steps 402.1-402.n mayalso report metrics related to other activities such as security checks.For example, time spent on a security check may be reported. Moreover,the number of items before and after a security check may also bereported. Likewise, result dependency manager 404 and dependency fetcherelements 406.1-406.m may also report metrics related to resultdependency fetching such as the processing time to fetch certaininformation.

FIG. 9 illustrates a diagrammatic representation of a machine 900 in theform of a computer system within which a set of instructions may beexecuted for causing the machine 900 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 9 shows a diagrammatic representation of the machine900 in the example form of a computer system, within which instructions916 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 900 to perform any one ormore of the methodologies discussed herein may be executed. For example,the instructions 916 may cause the machine 900 to execute any one ormore operations of any one or more of the methods described herein. Asanother example, the instructions 916 may cause the machine 900 toimplement portions of the data flows described herein. In this way, theinstructions 916 transform a general, non-programmed machine into aparticular machine 900 (e.g., the remote computing device 106, theaccess management system 118, the compute service manager 112, theexecution platform 114, the access management system 118, the Web proxy120, remote computing device 106) that is specially configured to carryout any one of the described and illustrated functions in the mannerdescribed herein.

In alternative embodiments, the machine 900 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 900 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 900 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 916, sequentially orotherwise, that specify actions to be taken by the machine 900. Further,while only a single machine 900 is illustrated, the term “machine” shallalso be taken to include a collection of machines 900 that individuallyor j ointly execute the instructions 916 to perform any one or more ofthe methodologies discussed herein.

The machine 900 includes processors 910, memory 930, and input/output(I/O) components 950 configured to communicate with each other such asvia a bus 902. In an example embodiment, the processors 910 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 912 and aprocessor 914 that may execute the instructions 916. The term“processor” is intended to include multi-core processors 910 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 916 contemporaneously. AlthoughFIG. 9 shows multiple processors 910, the machine 900 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 930 may include a main memory 932, a static memory 934, and astorage unit 936, all accessible to the processors 910 such as via thebus 902. The main memory 932, the static memory 934, and the storageunit 936 store the instructions 916 embodying any one or more of themethodologies or functions described herein. The instructions 916 mayalso reside, completely or partially, within the main memory 932, withinthe static memory 934, within the storage unit 936, within at least oneof the processors 910 (e.g., within the processor’s cache memory), orany suitable combination thereof, during execution thereof by themachine 900.

The I/O components 950 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 950 thatare included in a particular machine 900 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 950 mayinclude many other components that are not shown in FIG. 9 . The I/Ocomponents 950 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 950 mayinclude output components 952 and input components 954. The outputcomponents 952 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 954 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 950 may include communication components 964 operableto couple the machine 900 to a network 980 or devices 970 via a coupling982 and a coupling 972, respectively. For example, the communicationcomponents 964 may include a network interface component or anothersuitable device to interface with the network 980. In further examples,the communication components 964 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, and other communication components to provide communicationvia other modalities. The devices 970 may be another machine or any of awide variety of peripheral devices (e.g., a peripheral device coupledvia a universal serial bus (USB)). For example, as noted above, themachine 900 may correspond to any one of the remote computing device106, the access management system 118, the compute service manager 112,the execution platform 114, the Web proxy 120, and the devices 970 mayinclude any other of these systems and devices.

The various memories (e.g., 930, 932, 934, and/or memory of theprocessor(s) 910 and/or the storage unit 936) may store one or more setsof instructions 916 and data structures (e.g., software) embodying orutilized by any one or more of the methodologies or functions describedherein. These instructions 916, when executed by the processor(s) 910,cause various operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

In various example embodiments, one or more portions of the network 980may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 980 or a portion of the network980 may include a wireless or cellular network, and the coupling 982 maybe a Code Division Multiple Access (CDMA) connection, a Global Systemfor Mobile communications (GSM) connection, or another type of cellularor wireless coupling. In this example, the coupling 982 may implementany of a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1xRTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long-rangeprotocols, or other data transfer technology.

The instructions 916 may be transmitted or received over the network 980using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components964) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions916 may be transmitted or received using a transmission medium via thecoupling 972 (e.g., a peer-to-peer coupling) to the devices 970. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 916 for execution by the machine 900, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the methods described herein may be performedby one or more processors. The performance of certain of the operationsmay be distributed among the one or more processors, not only residingwithin a single machine, but also deployed across a number of machines.In some example embodiments, the processor or processors may be locatedin a single location (e.g., within a home environment, an officeenvironment, or a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

Described implementations of the subject matter can include one or morefeatures, alone or in combination as illustrated below by way ofexample.

Example 1. A method comprising: in response to receiving a show command,performing a plurality of navigation steps, each navigation stepcorresponding to a different layer in a database structure and eachnavigation step including an operator to fetch items from a metadatadatabase, the performing of the plurality of navigation steps including:in a first navigation step, fetching a first set of objects in a firstlayer of the database structure up to a first bounded limit; and in asecond navigation step, fetching a second set of objects in a secondlayer of the database structure corresponding to the first set ofobjects in the first layer up to a second bounded limit; fetchingdependency information associated with the second set of objects; andcompiling the dependency information associated with the second set ofobjects and generating results corresponding to the second set ofobjects.

Example 2. The method of example 2, further comprising: fetching a thirdset of objects using the second navigation step in the second layer ofthe database structure corresponding to the first set of objects up inthe first layer to a second bounded limit; fetching dependencyinformation associated with the third set of objects; and compiling thedependency information associated with the third set of objects andgenerating results corresponding to the third set of objects.

Example 3. The method of any of examples 1-2, further comprising:flushing memory used by the second navigation step.

Example 4. The method of any of examples 1-3, wherein the dependencyinformation associated with the second set of objects is fetched in asingle transaction with the metadata database using bulk reads.

Example 5. The method of any of examples 1-4, wherein the dependencyinformation associated with the third set of objects is fetched in thesingle transaction with the metadata database using bulk reads.

Example 6. The method of any of examples 1-5, wherein execution of theshow command is stopped before all objects corresponding to the showcommand in the first layer are fetched, and a bookmark is placedindicating where the show command was stopped.

Example 7. The method of any of examples 1-6, further comprising:executing a second show command based on the bookmark.

Example 8. The method of any of examples 1-7, further comprising:collecting metrics associated with each navigation step at a centralizedlocation.

Example 9. The method of any of examples 1-8, wherein the metricsinclude time spent fetching objects in each navigation step.

Example 10. The method of any of examples 1-9, further comprising:collecting metrics associated with fetching dependency information.

Example 11. A system comprising: one or more processors of a machine;and a memory storing instructions that, when executed by the one or moreprocessors, cause the machine to perform operations implementing any oneof example methods 1 to 10.

Example 12. A machine-readable storage device embodying instructionsthat, when executed by a machine, cause the machine to performoperations implementing any one of example methods 1 to 10.

1. A method comprising: receiving a show command; fetching a first setof first layer objects in a first layer of a metadata database up to afirst bounded limit using a first memory space; fetching a first set ofsecond layer objects in a second layer of the metadata databasecorresponding to the first set of first layer objects in the first layerup to a second bounded limit using a second memory space; fetchingdependency information associated with the first set of second layerobjects; flushing the second memory space; fetching a second set ofsecond layer objects in the second layer of the of the metadata databasecorresponding to the first set of first layer objects in the first layerup to the second bounded limit using the flushed second memory space;fetching dependency information associated with the second set of secondlayer objects; compiling dependency information associated with thefirst and second sets of second layer objects; and generating resultsfor the show command based on the compiled dependency information. 2.The method of claim 1, wherein the dependency information associated arespective layer is fetched in a single transaction with the metadatadatabase using bulk reads.
 3. The method of claim 1, wherein executionof the show command is stopped before all objects corresponding to theshow command in the first layer are fetched, and a bookmark is placedindicating where the show command was stopped.
 4. The method of claim 3,wherein the show command is a first show command, and the method furthercomprising: executing a second show command based on the bookmark. 5.The method of claim 1, further comprising: collecting metrics associatedwith each fetching step at a centralized location.
 6. The method ofclaim 5, wherein the metrics include time spent fetching objects.
 7. Themethod of claim 1, further comprising: collecting metrics associatedwith fetching dependency information.
 8. A machine-storage mediumembodying instructions that, when executed by a machine, cause themachine to perform operations comprising: receiving a show command;fetching a first set of first layer objects in a first layer of ametadata database up to a first bounded limit using a first memoryspace; fetching a first set of second layer objects in a second layer ofthe metadata database corresponding to the first set of first layerobjects in the first layer up to a second bounded limit using a secondmemory space; fetching dependency information associated with the firstset of second layer objects; flushing the second memory space; fetchinga second set of second layer objects in the second layer of the of themetadata database corresponding to the first set of first layer objectsin the first layer up to the second bounded limit using the flushedsecond memory space; fetching dependency information associated with thesecond set of second layer objects; compiling dependency informationassociated with the first and second sets of second layer objects; andgenerating results for the show command based on the compiled dependencyinformation.
 9. The machine-storage medium of claim 8, wherein thedependency information associated a respective layer is fetched in asingle transaction with the metadata database using bulk reads.
 10. Themachine-storage medium of claim 8, wherein execution of the show commandis stopped before all objects corresponding to the show command in thefirst layer are fetched, and a bookmark is placed indicating where theshow command was stopped.
 11. The machine-storage medium of claim 10,wherein the show command is a first show command, and the operationsfurther comprising: executing a second show command based on thebookmark.
 12. The machine-storage medium of claim 8, further comprising:collecting metrics associated with each fetching step at a centralizedlocation.
 13. The machine-storage medium of claim 12, wherein themetrics include time spent fetching objects.
 14. The machine-storagemedium of claim 8, further comprising: collecting metrics associatedwith fetching dependency information.
 15. A system comprising: at leastone hardware processor; and at least one memory storing instructionsthat, when executed by the at least one hardware processor, cause the atleast one hardware processor to perform operations comprising: receivinga show command; fetching a first set of first layer objects in a firstlayer of a metadata database up to a first bounded limit using a firstmemory space; fetching a first set of second layer objects in a secondlayer of the metadata database corresponding to the first set of firstlayer objects in the first layer up to a second bounded limit using asecond memory space; fetching dependency information associated with thefirst set of second layer objects; flushing the second memory space;fetching a second set of second layer objects in the second layer of theof the metadata database corresponding to the first set of first layerobjects in the first layer up to the second bounded limit using theflushed second memory space; fetching dependency information associatedwith the second set of second layer objects; compiling dependencyinformation associated with the first and second sets of second layerobjects; and generating results for the show command based on thecompiled dependency information.
 16. The system of claim 15, wherein thedependency information associated a respective layer is fetched in asingle transaction with the metadata database using bulk reads.
 17. Thesystem of claim 15, wherein execution of the show command is stoppedbefore all objects corresponding to the show command in the first layerare fetched, and a bookmark is placed indicating where the show commandwas stopped.
 18. The system of claim 17, wherein the show command is afirst show command, and the operations further comprising: executing asecond show command based on the bookmark.
 19. The system of claim 15,the operations further comprising: collecting metrics associated witheach fetching step at a centralized location.
 20. The system of claim19, wherein the metrics include time spent fetching objects.
 21. Thesystem of claim 15, further comprising: collecting metrics associatedwith fetching dependency information.